Holographic displays are ideal display technologies for virtual and augmented reality because all visual cues are provided. However, real-time high-quality holographic displays are difficult to achieve because the generation of high-quality computer-generated hologram (CGH) is inefficient in existing algorithms. Here, complex-valued convolutional neural network (CCNN) is proposed for phase-only CGH generation. The CCNN-CGH architecture is effective with a simple network structure based on the character design of complex amplitude. A holographic display prototype is set up for optical reconstruction. Experiments verify that state-of-the-art performance is achieved in terms of quality and generation speed in existing end-to-end neural holography methods using the ideal wave propagation model. The generation speed is three times faster than HoloNet and one-sixth faster than Holo-encoder, and the Peak Signal to Noise Ratio (PSNR) is increased by 3 dB and 9 dB, respectively. Real-time high-quality CGHs are generated in 1920×1072 and 3840×2160 resolutions for dynamic holographic displays.

Gratings inscribed in polymer optical fibre (POF) have attracted remarkable interest for many potential applications due to their distinctive properties. This paper overviews the current state of fabrication of POF gratings since their first demonstration in 1999. In particular we summarize and discuss POF materials, POF photosensitivity, techniques and issues of fabricating POF gratings, as well as various types of POF gratings.

In this paper, a hollow-core negative curvature fiber (HC-NCF) with high birefringence is proposed for low refractive index (RI) sensing based on surface plasmon resonance effect. In the design, the cladding region of the HC-NCF is composed of only one ring of eight silica tubes, and two of them are selectively filled with the gold wires. The influences of the gold wires-filled HC-NCF structure parameters on the propagation characteristic are investigated by the finite element method. Moreover, the sensing performances in the low RI range of 1.20–1.34 are evaluated by the traditional confinement loss method and novel birefringence analysis method, respectively. The simulation results show that for the confinement loss method, the obtained maximum sensitivity, resolution, and figure of merit of the gold wires-filled HC-NCF-based sensor are −5700 nm/RIU, 2.63 × 10⁻⁵ RIU, and 317 RIU⁻¹, respectively. For the birefringence analysis method, the obtained maximum sensitivity, resolution, and birefringence of the gold wires-filled HC-NCF-based sensor are −6100 nm/RIU, 2.56 × 10⁻⁵ RIU, and 1.72 × 10⁻³, respectively. It is believed that the proposed gold wires-filled HC-NCF-based low RI sensor has important applications in the fields of biochemistry and medicine.

In this paper, a surface plasmon resonance (SPR)-based photonic crystal fiber (PCF) sensor is proposed for simultaneously measuring the refractive index (RI) and temperature. In the design, the central air hole and external surface of the proposed PCF are coated with gold films, and an air hole is filled with the temperature-sensitive material (TSM). By introducing the inner and outer gold films and TSM, the RI and temperature can be measured simultaneously at different wavelength regions. The simulation results show that the average wavelength sensitivities of the proposed SPR-based PCF sensor can reach 4520 nm/RIU and 4.83 nm/°C in the RI range of 1.35~1.40 and a temperature range of 20~60 °C, respectively. Moreover, because of using the different wavelength regions for sensing, the RI and temperature detections of the proposed SPR-based PCF sensor can be achieved independently. It is believed that the proposed SPR-based PCF RI and temperature sensor has important applications in biomedicine and in environmental science.

Learning-based computer-generated hologram (CGH) demonstrates great potential for real-time high-quality holographic displays. However, real-time 4K CGH generation for 3D scenes remains a challenge due to the computational burden. Here, a variant conventional neural network (CNN) is presented for CGH encoding with learned layered initial phases for layered CGH generation. Specifically, the CNN predicts the CGH based on the input complex amplitude on the CGH plane, and the learned initial phases act as a universal phase for any target images at the target depth layer. These phases are generated during the training process of the coding CNN to further optimize the quality. The CNN is trained to learn encoding 3D CGH by randomly selecting the depth layer in the training process, and contains only 938 parameters. The generation time for a 2D 4K CGH is 18 ms, and is increased by 12 ms for each layer in a layered 3D scene. The average Peak Signal to Noise Ratio (PSNR) of each layer is above 30dB in the depth range from 160 to 210 mm. Experiments verify that our method can achieve real-time layered 4K CGH generation.

Targeting the huge unused bandwidth (BW) of modern telecommunication networks, Bi/Er co-doped silica optical fibers (BEDFs) have been proposed and developed for ultra-broadband, high-gain optical amplifiers covering the 1150–1700 nm wavelength range. Ultrabroadband luminescence has been demonstrated in both BEDFs and bismuth/erbium/ytterbium co-doped optical fibers (BEYDFs) fabricated with the modified chemical vapor deposition (MCVD) and in situ doping techniques. Several novel and sophisticated techniques have been developed for the fabrication and characterization of the new active fibers. For controlling the performance of the active fibers, post-treatment processes using high temperature, γ-radiation, and laser light have been introduced. Although many fundamental scientific and technological issues and challenges still remain, several photonic applications, such as fiber sensing, fiber gratings, fiber amplification, fiber lasers, etc., have already been demonstrated.